### Objective function

What will be minimised or maximised, e.g. present value.

### Restriction

Condition that limits the selected treatment alternatives. For example: a requirement for a specified area of mature forest in each period.

### Parameter

Value from a simulation, e.g. harvested volume in a certain period from a certain stand. This is also termed a coefficient in an LP-problem.

### Decision variable

The result of an optimisation model = selected management program for each stand

### Account variable

For example, the sum of a parameter multiplied by the decision variable for each stand, e.g. total harvest in a period.

### Set

The amount. For example, all stands comprise one amount, and all periods another. Set makes the optimisation model easy to survey.

### Index

An element in a “set” is represented by an index, e.g. v[I j p] = volume of stand I at a given time p, under management program j.

### Modelling language

A modelling tool with its own “language” for formulating an optimisation problem. Heureka uses ZIMPI for this. The tool compiles the optimisation model to enable its solution with a solver.

### Solvers

Heureka creates input data for five third party optimisation programs: LP_Solve, CPLEX, Gurobi, MOSEK and SCIP**. You can read more about the solvers here:https://www.heurekaslu.se/wiki/Category:Optimization#About_the_optimization_tool